The CircaHealth CircaPain study protocol: A longitudinal multi-site study of the chronobiological control of chronic pain
Bibliographic record
Abstract
Abstract Introduction One in five Canadians lives with chronic pain. Evidence shows that some individuals experience pain that fluctuates in intensity following a circadian (24-hour) rhythm. Endogenous molecular rhythms regulate the function of most physiological processes, neuroimmunology functions that govern pain mechanisms. Addressing chronic pain rhythmicity on a molecular and biopsychosocial level can advance understanding of the disease and identify new treatment/management strategies. Our CircaHealth CircaPain study uses an online survey combined with ecological momentary assessments and bio-sample collection to investigate the circadian control of chronic pain and identify potential biomarkers. Our primary objective is to understand inter-individual variability in pain rhythmicity, by collecting biopsychosocial measures. The secondary objective accounts for seasonal variability and the effect of latitude on rhythmicity. Methods and analysis Following completion of a baseline questionnaire, participants complete a series of electronic symptom-tracking diaries to rate their pain intensity, negative affect, and fatigue on a 0-10 scale at 8:00, 14:00, and 20:00 daily over 10 days. These measures are repeated at 6- and 12-months post-enrolment to account for potential seasonal changes. Infrastructure is being developed to facilitate the collection of blood samples from subgroups of participants 2 times per day over 24-48 hours to identify rhythmic expression of circulating genes and/or proteins. Ethics and dissemination Ethical approval for this study was obtained by the Queen’s University Health Sciences and Affiliated Teaching Hospitals Research Ethics Board. Findings will be published in a relevant scientific journal and disseminated at national and international scientific meetings and online webinars. We maintain a website to post updated resources and engage with the community. We employ knowledge mobilization in the form of direct data sharing with participants. This study is funded by the Canadian Institutes of Health Research (CIHR) (grant PJT-497592) and the CIHR Strategy for Patient-Oriented Research (SPOR) Chronic Pain Network (CPN) (grant SCA-145102). Ethical approval date: 08 March 2024 Estimated start of the study: April 2024 Strengths and limitations of this study Data will be collected using self-report questionnaires only, which may lead to random or systematic misreporting. The online nature of the study might affect the diversity in our sample (e.g., the representation of rural and/or underprivileged communities). Physical distance from research laboratories with specialized equipment for analyses and biobanking storage might affect accessibility, however, this can be overcome by using mailable dried blood spot collection kits as described. Questionnaires used in our study have previously been validated in the chronic pain population and used in several languages. Uncovering distinct pain rhythmicity patterns and health outcomes associated with rhythmicity may help develop new treatments for different chronic pain conditions tailored to individual circadian rhythms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".